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global_sr_module

Introduction

This module can be used to calculate root zone storage capacities globally.

Installation

This module requires a linux environment because it uses python packages that are only compatible with linux. When you are working on windows10, you could use WSL. Detailed steps on how to use this module in WSL are provided here: https://docs.google.com/document/d/1-NzAk0YgFRNr7qcqgXqP1tij6xgiNaLK2S2uuHJIlV4/edit?usp=sharing

The basic steps to use this module in miniconda are as follows:

  1. Install miniconda in your home directory and activate (https://docs.conda.io/en/latest/miniconda.html)
  1. Clone the files in this git repository to your home directory
  1. Create your conda environment
  • The git repository contains a sr_environment.yml file. This is the conda environment with all the required packages.
  • Install the sr_environment: conda env create --file sr_environment.yml
  • Activate your environment: conda activate sr_env

Data

The following input data is needed:

An example dataset can be downloaded from: https://surfdrive.surf.nl/files/index.php/s/GiBTx1WYBDaoYmJ (data.zip)

Running

The module consists of three run-scripts (jupyter notebooks) and five function scripts (.py)

run scripts

  1. run_script_main is the main run script that guides you through the entire calculation procedure.
  2. run_script_grid_to_catchments is used to extract catchment timeseries from the global gridded data. run_script_main tells you when to use this script.
  3. run_script_earth_engine is used to extract catchment timeseries from satellite products using google earth engine. run_script_main tells you when to use this script.

function scripts

  1. f_preprocess_discharge -> preprocessing GSIM discharge data
  2. f_grid_to_catchments -> extract catchment timeseries from gridded products
  3. f_earth_engine -> extract catchment average values from satellite products using Google Earth Engine
  4. f_sr_calculation -> calculate catchment root zone storage capacity based on catchment water balances
  5. f_catch_characteristics -> calculate catchment climate and landscape characteristics and store in table
  6. f_regression -> linear regression model to predict sr based on descriptor parameters

Examples

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  • Python 59.5%
  • Jupyter Notebook 40.5%